---

#### Theory lessons <br>

| Marta Coronado Zamora | Jose F. Sánchez | 
|:-:|:-:|
| <a href="mailto:Marta.coronado@uab.cat"><i class="fa fa-paper-plane fa-fw"></i> marta.coronado@uab.cat</a> | <a href="mailto:JoseFrancisco.Sanchez@uab.cat"><i class="fa fa-paper-plane fa-fw"></i>&nbsp; josefrancisco.sanchez@uab.cat</a> | 
| <a href="https://bsky.app/profile/geneticament.bsky.social"><i class="fab fa-bluesky fa-fw"></i>&nbsp; @geneticament</a> |                 <a href="https://twitter.com/JFSanchezBioinf"><i class="fab fa-twitter fa-fw"></i>&nbsp; @JFSanchezBioinf</a> |
| <a href="https://portalrecerca.uab.cat/es/organisations/grup-de-gen%C3%B2mica-bioinform%C3%A0tica-i-biologia-evolutiva-gbbe/"><i class="fa fa-map-marker fa-fw"></i>&nbsp; Universitat Autònoma de Barcelona </a> |    <a href="http://www.germanstrias.org/technology-services/genomica-bioinformatica/"> <i class="fa fa-map-marker fa-fw"></i>Germans Trias i Pujol Research Institute (IGTP)</a> |

#### Practical lessons <br>

| Miriam Merenciano |
|:-:|
| <a href="mailto:miriam.merenciano@uab.cat"><i class="fa fa-paper-plane fa-fw"></i> miriam.merenciano@uab.cat </a> | 
|  <a href="https://portalrecerca.uab.cat/es/organisations/grup-de-gen%C3%B2mica-bioinform%C3%A0tica-i-biologia-evolutiva-gbbe/"><i class="fa fa-map-marker fa-fw"></i>&nbsp; Universitat Autònoma de Barcelona </a> |

---
layout: true
class: animated, fadeIn

---

# Session content

- Biological data
- General statistical graphics used in bioinformatics
- Specialized libraries and software
    + Static
    + Interactive
- Creating plots with data from papers

---
layout: false
class: left, bottom, inverse, animated, bounceInDown

# Get started!
## Tools for data visualization in bioinformatics

---

---
# Biological data

- __Quantitative and qualitative data__: scatterplots, barplots, boxplots, heatmaps, ...
- __Molecular sequences__: alignments, motifs, genome browsers, ...
- __Species relationships__: trees, networks
- __Molecular pathways and interactions__: cell diagrams, networks, ...
- __Molecular structures__: 3D molecular viewers, ...
- __Anatomical structures__: anatograms, ...
- __...__

#### <i class="fas fa-pencil-alt"></i> **Exercise** | Go to the research articles from [Nature Communications](https://www.nature.com/search?journal=ncomms&article_type=research&date_range=last_5_years&order=date_asc) journal and describe the types of visualizations used. How far can you enlarge the previous list?

Use the collaborative **PADLET** available in Atenea:  https://atenea.upc.edu/course/section.php?id=907338

---

## General statistical graphics

### Biological example

Gene expression data from [GTEx project](https://gtexportal.org/home/):

``` r
data <- read.table(file = "https://raw.githubusercontent.com/marta-coronado/data_visualization/refs/heads/main/T/2/expression_data.txt", 
                   header = TRUE, sep ="\t", stringsAsFactors = FALSE)
```

<div style="font-size: 60%;">
<div class="datatables html-widget html-fill-item" id="htmlwidget-726214b06cc12e03edec" style="width:100%;height:auto;"></div>
<script type="application/json" data-for="htmlwidget-726214b06cc12e03edec">{"x":{"filter":"none","vertical":false,"data":[["1","2","3","4","5","6","7","8","9","10"],["ENSG00000000003.10","ENSG00000000003.10","ENSG00000000003.10","ENSG00000000003.10","ENSG00000000003.10","ENSG00000000003.10","ENSG00000000003.10","ENSG00000000003.10","ENSG00000000005.5","ENSG00000000005.5"],["TSPAN6","TSPAN6","TSPAN6","TSPAN6","TSPAN6","TSPAN6","TSPAN6","TSPAN6","TNMD","TNMD"],["Brain","Liver","Muscle","Testis","Adipose","Lymphocytes","Lung","Stomach","Adipose","Muscle"],[5.8635,30.78,2.5905,97.09,39.71,0.10475,17.49,14.775,26.805,0.32085],["Brain","Liver","Muscle","Testis","Adipose.Tissue","Blood","Lung","Stomach","Adipose.Tissue","Muscle"],["X","X","X","X","X","X","X","X","X","X"],["HAVANA","HAVANA","HAVANA","HAVANA","HAVANA","HAVANA","HAVANA","HAVANA","HAVANA","HAVANA"],[99883667,99883667,99883667,99883667,99883667,99883667,99883667,99883667,99839799,99839799],[99894988,99894988,99894988,99894988,99894988,99894988,99894988,99894988,99854882,99854882],["-","-","-","-","-","-","-","-","+","+"],["protein_coding","protein_coding","protein_coding","protein_coding","protein_coding","protein_coding","protein_coding","protein_coding","protein_coding","protein_coding"],["KNOWN","KNOWN","KNOWN","KNOWN","KNOWN","KNOWN","KNOWN","KNOWN","KNOWN","KNOWN"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>gene_id<\/th>\n      <th>gene_name<\/th>\n      <th>tissue<\/th>\n      <th>median_expression<\/th>\n      <th>tissue_type<\/th>\n      <th>gene_chr<\/th>\n      <th>gene_annotation<\/th>\n      <th>gene_start<\/th>\n      <th>gene_end<\/th>\n      <th>gene_strand<\/th>\n      <th>gene_type<\/th>\n      <th>gene_status<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"scrollY":300,"scrollX":600,"pageLength":25,"fontSize":"5%","dom":"ti","ordering":false,"rownames":false,"options":{"pageLength":5,"scrollX":true},"class":"white-space: nowrap","columnDefs":[{"className":"dt-right","targets":[4,8,9]},{"orderable":false,"targets":0},{"name":" ","targets":0},{"name":"gene_id","targets":1},{"name":"gene_name","targets":2},{"name":"tissue","targets":3},{"name":"median_expression","targets":4},{"name":"tissue_type","targets":5},{"name":"gene_chr","targets":6},{"name":"gene_annotation","targets":7},{"name":"gene_start","targets":8},{"name":"gene_end","targets":9},{"name":"gene_strand","targets":10},{"name":"gene_type","targets":11},{"name":"gene_status","targets":12}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
</div>
---

## Biological example

The `data` data frame contains the expression of 52302 genes in 8 tissues.

|               | Adipose| Brain| Liver|  Lung| Lymphocytes| Muscle| Stomach| Testis|
|:--------------|-------:|-----:|-----:|-----:|-----------:|------:|-------:|------:|
|antisense      |    4999|  4999|  4999|  4999|        4999|   4999|    4999|   4999|
|lincRNA        |    7033|  7033|  7033|  7033|        7033|   7033|    7033|   7033|
|miRNA          |    2838|  2838|  2838|  2838|        2838|   2838|    2838|   2838|
|misc_RNA       |    2010|  2010|  2010|  2010|        2010|   2010|    2010|   2010|
|protein_coding |   19820| 19820| 19820| 19820|       19820|  19820|   19820|  19820|
|pseudogene     |   13705| 13705| 13705| 13705|       13705|  13705|   13705|  13705|
|rRNA           |     520|   520|   520|   520|         520|    520|     520|    520|
|snoRNA         |    1408|  1408|  1408|  1408|        1408|   1408|    1408|   1408|
|snRNA          |    1892|  1892|  1892|  1892|        1892|   1892|    1892|   1892|

---

## Biological example

#### <i class="fas fa-pencil-alt"></i> **Exercise** |  Use `ggplot2` to answer the following questions:

- Which type of gene is more expressed on average?

<div style="background-color:#F0F0F0">
&emsp;<i class="fas fa-comment-dots"></i>
Answer:

&emsp;
</div>

- Which tissue has more expressed genes?

<div style="background-color:#F0F0F0">
&emsp;<i class="fas fa-comment-dots"></i>
Answer:

&emsp;
</div>

- In which tissue is HLA-B gene expressed the most? Is its expression low, high or average compared to other genes?

<div style="background-color:#F0F0F0">
&emsp;<i class="fas fa-comment-dots"></i>
Answer:

&emsp;
</div>

- In which chromosomes are located the genes highly expressed in brain (median\_expression >20,000)?

<div style="background-color:#F0F0F0">
&emsp;<i class="fas fa-comment-dots"></i>
Answer:

&emsp;
</div>

---

## Biological example

#### <i class="fas fa-pencil-alt"></i> **Exercise** |  Use `ggplot2` to answer the following questions:

- Which type of gene is more expressed on average?

- What variables do we want in the `x`-axis and in the `y`-axis?
  - What `geom_()` is the most appropriate?

---

## Biological example

#### <i class="fas fa-pencil-alt"></i> **Exercise** |  Use `ggplot2` to answer the following questions:

- Which type of gene is more expressed on average?

``` r
ggplot(data, aes(x=gene_type, 
                 y=log10(median_expression))) +
  geom_boxplot() +
# scale_y_log10() + # another possibility for the log
  stat_summary(fun.y = "mean", 
               geom = "point",
               color = "maroon")
```
]
.pull-right[
<img src="data:image/png;base64,#2_sol_files/figure-html/unnamed-chunk-5-1.png" alt="" width="720" style="display: block; margin: auto;" />
]

<div style="background-color:#F0F0F0">
&emsp;<i class="fas fa-comment-dots"></i>
Answer:
&emsp;
Protein-coding genes are the most expressed.
</div>

---
## Biological example

- Which tissue has more expressed genes?

- What `geom_()` is the most appropriate?
  - What variables do we want in the `x`-axis and in the `y`-axis?

``` r
library(dplyr)

# we filter the genes that are expressed:
data_expr <- data %>% filter(median_expression > 0)
```

---
## Biological example

``` r
ggplot(data_expr, 
       aes(x=tissue)) +
  geom_bar()
```
]

.pull-right[
<img src="data:image/png;base64,#2_sol_files/figure-html/unnamed-chunk-8-1.png" alt="" width="720" style="display: block; margin: auto;" />
]

<div style="background-color:#F0F0F0">
&emsp;<i class="fas fa-comment-dots"></i>
Answer:
The tissue with more expresed genes is testis.
&emsp;
</div>

---

## Biological example

- In which tissue is HLA-B gene expressed the most? Is its expression low, high or average compared to other genes?

- What `geom_()` is the most appropriate?
  - What variables do we want in the `x`-axis and in the `y`-axis?

``` r
library(dplyr)

data_gene <- data %>%
  filter(gene_name %in% "HLA-B")
```

<div class="datatables html-widget html-fill-item" id="htmlwidget-9caa9ddafa0490e3f58b" style="width:100%;height:auto;"></div>
<script type="application/json" data-for="htmlwidget-9caa9ddafa0490e3f58b">{"x":{"filter":"none","vertical":false,"data":[["1","2","3","4","5","6","7","8"],["ENSG00000234745.5","ENSG00000234745.5","ENSG00000234745.5","ENSG00000234745.5","ENSG00000234745.5","ENSG00000234745.5","ENSG00000234745.5","ENSG00000234745.5"],["HLA-B","HLA-B","HLA-B","HLA-B","HLA-B","HLA-B","HLA-B","HLA-B"],["Muscle","Testis","Brain","Liver","Lymphocytes","Stomach","Lung","Adipose"],[219.4,396.9,168.25,467.5,5285.5,810.15,4566,2174.5],["Muscle","Testis","Brain","Liver","Blood","Stomach","Lung","Adipose.Tissue"],["6","6","6","6","6","6","6","6"],["HAVANA","HAVANA","HAVANA","HAVANA","HAVANA","HAVANA","HAVANA","HAVANA"],[31321649,31321649,31321649,31321649,31321649,31321649,31321649,31321649],[31324219,31324219,31324219,31324219,31324219,31324219,31324219,31324219],["-","-","-","-","-","-","-","-"],["protein_coding","protein_coding","protein_coding","protein_coding","protein_coding","protein_coding","protein_coding","protein_coding"],["KNOWN","KNOWN","KNOWN","KNOWN","KNOWN","KNOWN","KNOWN","KNOWN"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>gene_id<\/th>\n      <th>gene_name<\/th>\n      <th>tissue<\/th>\n      <th>median_expression<\/th>\n      <th>tissue_type<\/th>\n      <th>gene_chr<\/th>\n      <th>gene_annotation<\/th>\n      <th>gene_start<\/th>\n      <th>gene_end<\/th>\n      <th>gene_strand<\/th>\n      <th>gene_type<\/th>\n      <th>gene_status<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"scrollY":300,"scrollX":600,"pageLength":5,"fontSize":"5%","dom":"ti","ordering":false,"rownames":false,"options":{"pageLength":5,"scrollX":true},"class":"white-space: nowrap","columnDefs":[{"className":"dt-right","targets":[4,8,9]},{"orderable":false,"targets":0},{"name":" ","targets":0},{"name":"gene_id","targets":1},{"name":"gene_name","targets":2},{"name":"tissue","targets":3},{"name":"median_expression","targets":4},{"name":"tissue_type","targets":5},{"name":"gene_chr","targets":6},{"name":"gene_annotation","targets":7},{"name":"gene_start","targets":8},{"name":"gene_end","targets":9},{"name":"gene_strand","targets":10},{"name":"gene_type","targets":11},{"name":"gene_status","targets":12}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>

</div>

---

## Biological example

.pull-left[
- In which tissue is HLA-B gene expressed the most? Is its expression low, high or average compared to other genes?

``` r
ggplot(data, aes(x=tissue,
                 y=log10(median_expression))) +
  geom_boxplot() +
  geom_point(data = data_gene, 
             colour= "maroon")
```
]
.pull-right[
<img src="data:image/png;base64,#2_sol_files/figure-html/unnamed-chunk-12-1.png" alt="" width="720" style="display: block; margin: auto;" />
]

<div style="background-color:#F0F0F0">
&emsp;<i class="fas fa-comment-dots"></i>
Answer:
In all tissues is highly expressed.
&emsp;
</div>

---

## Biological example

- In which chromosomes are located the genes highly expressed in brain (median\_expression >20,000)?

- What `geom_()` is the most appropriate?
  - What variables do we want in the `x`-axis and in the `y`-axis?

``` r
data_brain <- data %>%
  filter(tissue == "Brain" &
           median_expression > 0)
```

---

## Biological example

.pull-left[
- In which chromosomes are located the genes highly expressed in brain (median\_expression >20,000)?

``` r
ggplot(data_brain, 
       aes(x=gene_chr, 
           y=median_expression, 
           colour=median_expression > 20000)) +
  geom_point() +
  scale_color_manual(
    values=c("orange2", 
             "cadetblue3"))
```
]

.pull-right[
<img src="data:image/png;base64,#2_sol_files/figure-html/unnamed-chunk-15-1.png" alt="" width="720" style="display: block; margin: auto;" />

]
<div style="background-color:#F0F0F0">
&emsp;<i class="fas fa-comment-dots"></i>
Answer:
In the mitochondrial genome
&emsp;
</div>
---

# Specialised libraries and software

- Integrated software suites
- Javascript
    + [BioJS](https://biojs.net)
- R libraries
    + Specialised repositories [bioconductor](https://bioconductor.org)
    + [`ggplot2` extensions](https://exts.ggplot2.tidyverse.org/gallery/)
    + [`htmlwidgets`](http://gallery.htmlwidgets.org/), some using BioJS libraries

#### <i class="fas fa-pencil-alt"></i> **Exercise** |  Which `ggplot2` extensions and `htmlwidgets` are designed to cover specific needs of biological data?

<div style="background-color:#F0F0F0">
&emsp;<i class="fas fa-comment-dots"></i>
Answer:

&emsp;
</div>

Use the collaborative **PADLET** available in Atenea:  https://atenea.upc.edu/course/section.php?id=907340

---

## Static visualizations: `ggplot2` extensions

### Phylogenetic trees: `ggtree`

``` r
library(ggtree)
set.seed(10); tr <- rtree(20)

ggtree(tr, colour = "darkgreen", alpha = 0.8, size = 1.5)+
    scale_y_continuous(breaks = 1:20, position = "right", name = "edges") + 
    annotate(geom = "text", x = 0.5, y = 19, label = "It's a ggplot!") 
```

---

### Anatomical structures: `gganatogram`

``` r
library(gganatogram)

gganatogram(data=organ_df, fillOutline='#a6bddb', organism='human',
            sex='female', fill="value") +
    scale_fill_gradient(low = "white", high = "red") + 
    facet_wrap(~ type) 
```

---

### Sequence logos: `ggseqlogo`

``` r
library(ggseqlogo)

ggplot() + 
  geom_logo(seqs_dna$MA0002.1) +
  theme_logo() + 
  labs(title = "My TFBS profile")
```

---
### Gene structures: `gggenes` y `gggenomes`

``` r
library(gggenes)
ggplot(example_genes, aes(xmin = start, xmax = end, y = molecule, fill = gene)) +
  geom_feature(
    data = example_features,
    aes(x = position, y = molecule, forward = forward)
    ) +
  geom_feature_label(
    data = example_features,
    aes(x = position, y = molecule, label = name, forward = forward)
    ) +
  geom_gene_arrow() +
  geom_blank(data = example_dummies) +
  facet_wrap(~ molecule, scales = "free", ncol = 1) +
  scale_fill_brewer(palette = "Set3") +
  theme_genes()
```
]

.pull-right[
<img src="img/Captura.PNG" width=100%>
<small>
Figure 7 - Tahami, M.S., Vargas-Chavez, C., Poikela, N., Coronado-Zamora, M., et al. Transposable elements in Drosophila montana from harsh cold environments. Mobile DNA 15, 18 (2024). https://doi.org/10.1186/s13100-024-00328-7
]

---

### Show intersections: `UpSetR`

``` r
library(UpSetR)

mutations <- read.csv( system.file("extdata", "mutations.csv", package = "UpSetR"), header=T, sep = ",")

upset(mutations, sets = c("PTEN", "TP53", "EGFR", "PIK3R1", "RB1"), sets.bar.color = "#56B4E9",
order.by = "freq", empty.intersections = "on")
```

---

## Interactive visualizations

### Multiple alignment: `msaR`

``` r
library(msaR)
seqfile <- system.file("sequences","AHBA.aln", package="msaR")
msaR(seqfile)
```

<div class="msaR html-widget html-fill-item" id="htmlwidget-e1196c468d7bd1dbd2eb" style="width:50%;height:252px;"></div>
<script type="application/json" data-for="htmlwidget-e1196c468d7bd1dbd2eb">{"x":{"sequences":[{"id":"Hygrocin_AHBA","name":"Hygrocin_AHBA","seq":"--atg--------------------------------------------------------agcaatgatgtgcgactgcga-------tccgagctgcccgcatggccgcagtacggcgacgaggagcgcgaggccctcatccgggctctggatcaggggcaatggtggcgtatcgggggcggtgaggtcgacgccttcgaggcggagttcgccgcggcccatgggagcgagcacgccctggcggtcaccaatgggacgcatgcgctggagctcgccctcgaagtgctcggggtcggcaccgactccgaggtgatcgttcccgcgttcaccttcatctcgtcctcgcaggcggctcagcggctgggcgcggtggccgtgcccgtggacgtggacccggacacgtactgcatcgatccctcggcggtcgaggcagccatcggcccgaaaacccgcgcgatcatgccggtgcacatggcgggccagatgtgcgacatggacgcgctgggcaagctgtccgccgactcgggggtgccgctgatccaggacgcggcccatgcgcacggtgcgcggtggcgcggtcagaaggtcggtgagctgggctcggtcgccgcgttcagcttccagaacggaaagctgatgacggccggtgagggcggcgccgtgctcttccccgatgccgagatgtacgagaggggcttcgtccggcacagctgtggacgtccgcgcaccgaccgcggctacttccaccgcacctcgggctccaacttccggctgaacgagttctccgcatccgtactgcgcgcccaactcgcccgcctggacggccagatcaccacgcgtgagcagcgctggccggtgctgagccggctgctcgccgaaatccccggtgtggtaccgcagtcgcgcgacgaccgcggtgaccgcaatccgcactacatggcgatgttccgggtgccgggcatcaccgaggagcgccgcgcgaaggtcgtcgacaccctcatcgagcgcggggtgcccgcgtttgtcgcgttccgcgcggtctaccgtacggacgccttctgggaggtcgcggcgccggatctgacggtggacgaactcgcccgccgctgcccgcactccgaggcgctcacccgcgactgcctttggctgcaccaccgggtgctgctgggcagcgaggagcagatgcacgaagtggccgccgtcgtcgccgatgtgctcgcgggcgcatga"},{"id":"Macbecin_AHBA","name":"Macbecin_AHBA","seq":"--atg-----------------------------------------------------------agcaattctccggacgca----ggcatcgagttcccggcctggccgcagcacgacgacgccgagcgcacggccctgctgcgcgcgctggaccagggccagtggtggcgggtcggcggcagcgaggtcgacgagttcgagcgcgagttcgccgagtaccacggcgcgggccacgcgctcgccgtcaccaacgggacgcacgcgctggagctggcgctccaggtgctcgacgtgggccccggcgccgaggtgatcgtgcccgccttcaccttcatctcgtcctcgcaggccgtgcagcgcctcggcgcggtcgccgtgcccgtggacgtcgaccccgacacctactgcctggacgtcgccgccgccgaggccgccgtcacctcccgcacctcggcgatcatgcccgtgcacatggcgggccagttcgccgacatggaccggctggacaagctgtccacgagcaccggcgtgccggtcctgcaggacgccgcccacgcccacggcgcccactggcgcggcaagcgggtcggcgagctcgggtcgatcgccacgttcagcttccagaacggcaagctgatgacggcgggcgagggcggggccgtgctgttcgaggacgaggcccagtgggagaaggcgttcgtcctgcacagctgcggcaggcccaagggcgaccgcggctacttccacctcacctcgggctcgaacttccggatgaacgagttctccgccgccgtgctgcgcgcccagctcggcaggctggactcgcagatcgcgaccaggcaggcccgctggccggtgctgtccgcgctgctggcggggatcgacggcgtcgtgccgcagaccgtcgacccgcgcagcgaccgcaacccgagctacatggcgatgttccggatgcccggcgtcaccgaggagcggcgcaacgcggtggtggacgagctggtgcggcgcgggatcccggcgttcatggcgttccgggcggtctaccgcacccaggcgttctgggagaccggcgcgcccgacctgaccaccgaggagctggccgcccggtgcccggtgtcggaggagatcacccgcgactgcgtgtggctgcaccaccgggtgctgctgggcaccgaggagcagctgcaccggcttgccgccgtcgtggccgacgtggtcgcgggcgcgtga"},{"id":"Ansatrienin_AHBA","name":"Ansatrienin_AHBA","seq":"--gtg--------------------------------------------------------cacctgaaggccccgcagaag-------ctggagttccccgcctggccgcagttcgactcgaccgagcgcgaggccctgctgcgcacgctcgacagcggccggtggtggcggatcaccggggacgaggtcgtctcgttcgagcgggagttcgcgcaggcccacggcgcccggcacgcgctcgccgtcaccacgggcacgcacgccctcgaactcgtcctggaggtcatgggcgccgggcctggcaccgaggtgatcgtgcccgcgttcaccttcatctcctcctcgcaggccgcccagcggctcggcgcggtcgtcgtccccgtcgacgtcgacccggacacctactgcgtcgacccggccgccgtgcgggccgcgatcaccgagcgcaccagggcgatcatgcccgtgcacatggcgggccagctcgccgacatggacgcgctgcgcgccctgtccgccgagaccggcgtcccgctgctccaggacgccgcgcacgcccacggcgcgcggtggcgcggcgagcccctcggcgcgctcggctcgatcgccgccttcagcttccagaacggcaagctgatgaccgcgggcgagggcggcgccgtgctgctcccggacggggaccagcacgaggaggcgttcctgcgccacgcctgcgggcgcccgctcgccgaccgggcctaccaccaccgcacggccgggtcgaacttccgcatgaacgagttctccgccgcgctgctgcgcgcccagctcggcaggctccagcagcagaccgacgtgcgcgagcggcactggcccgtgctgtcgcggctgctcgccgaggtgcccggcgtcgtcccgcaggggaccgacccgcgctgcgaccgcaacccgcactacatggccatgttccaggtccccggcatcggcgaggagcgccgcgcccgcctggtcgacgcgctggtcgagcgcggagtcccggcgttcgccgggttccgcgcgatctaccgcaccgccgcgttctgggagcgcgccgcccccgacaccacggtggacgccctcgcccgcgcctgcccggtcacggaagcgctcagcagcgactgcgtgtggctgcaccaccgggtgctgctgggggcggaggagcagatgccgctggtggcggaggccgtcgcctgcgcgctggaggaggcctga"},{"id":"Ansamitocin_AHBA","name":"Ansamitocin_AHBA","seq":"--gtg--------------------------------------------------------cacctgaaggccccgcagaag-------ctggagttccccgcctggccgcagttcgactcgaccgagcgcgaggccctgctgcgcacgctcgacagcggccggtggtggcggatcaccggggacgaggtcgtctcgttcgagcgggagttcgcgcaggcccacggcgcccggcacgcgctcgccgtcaccacgggcacgcacgccctcgaactcgtcctggaggtcatgggcgccgggcctggcaccgaggtgatcgtgcccgcgttcaccttcatctcctcctcgcaggccgcccagcggctcggcgcggtcgtcgtccccgtcgacgtcgacccggacacctactgcgtcgacccggccgccgtgcgggccgcgatcaccgagcgcaccagggcgatcatgcccgtgcacatggcgggccagctcgccgacatggacgcgctgcgcgccctgtccgccgagaccggcgtcccgctgctccaggacgccgcgcacgcccacggcgcgcggtggcgcggcgagcccctcggcgcgctcggctcgatcgccgccttcagcttccagaacggcaagctgatgaccgcgggcgagggcggcgccgtgctgctcccggacggggaccagcacgaggaggcgttcctgcgccacgcctgcgggcgcccgctcgccgaccgggcctaccaccaccgcacggccgggtcgaacttccgcatgaacgagttctccgccgcgctgctgcgcgcccagctcggcaggctccagcagcagaccgacgtgcgcgagcggcactggcccgtgctgtcgcggctgctcgccgaggtgcccggcgtcgtcccgcaggggaccgacccgcgctgcgaccgcaacccgcactacatggccatgttccaggtccccggcatcggcgaggagcgccgcgcccgcctggtcgacgcgctggtcgagcgcgga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cgcgatcagcgctggacgctgctgtcccggctgctcggtgagatcgacggcgtcgtaccccagggcagcgacccgcgcgccgaccggaactcccactacatggcgatgttccggatccccggcatatccgaggaggcccgcaacgccctcgtcgacacgctcgtcgaggccggcctgcccgccttcgccgccttccgggcgatctaccgcaccgacgcgttctgggagacggccgcgcccgacaccaccgtcgacaagctcgccgaaagctgcccgcacaccgaggcgatcagcaccgactgcatctggctgcaccatcgagtgctgctcgcctcggaggaggccctccacaccacagccgagatcatcgccgacgccgtggccgcacggtga"},{"id":"Ansalactam_AHBA","name":"Ansalactam_AHBA","seq":"---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------gcgccgcgcatgctttggccgtcaccaacggcacccatgccctggaactggcgctgcagtgtctgggtgtcggtccgggcacggaggtcatcgtgccggccttcactttcatctcctcgtcccaggccgctcagcggctcggcgcggtcgccgtccccgtcgacgtcgatctcgacacgtacaacatcgacgtggctgccgcggcggccgccgtcacccctcgcaccagggtgatcatgcccgtgcacatggcggggctcatcgctgacatggacgcgctcgacaagctctccgcggacaccggcgtgcccgtgctgcaggacgccgcccacgcgcacggtgcccgatggcagggcaaacgggtgggcgagttgggcacggtcgccaccttcagcttccagaacggcaagctgatgaccgccggcgagggtggtgccctgctcttccccgacgaggagacgtacgaggccgccttcctgcggcacagctgtggccggccgcgcaccgaccgccactacatgcaccagaccgccggcaccaacatgcggctcaacgagttctctgcctccgtgctccgcgcccaactgagccgcctcgacgcccagctcacgctgcgcgatgagcgctggacactgctgtcccggctgctcggcaagatcgacggcgtcgtaccccaggggggcgatgcgcgcgcc------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"},{"id":"Arifamycini","name":"Arifamycini","seq":"--atg--------------------------------------------------------------aacgcacgacaggcg-------cccgaattcccccaatggccgcagtacgacgacgcagagcgggacggcctgatccgtgcgctggagcagggccagtggtggcgcatgggcggcagcgaggtggacgcgttcgagcacgagttcgccgcgcaccacggcgccgcgcacgcgctggcggccaccaccgggacgcacgcgctcgaactcgccctgcagtgcatgggcgtcgggccgggcaccgaggtgatcgtgccggccttcaccttcatctcctcctcccaggcggcccagcggctcggcgcggtcgcggtcccggtcgacgtcgacgccggcacctacaacctcgacccggcggcggtctccgccgccctcaccccccgcacccgcgtgatcatgccggtgcacatggccgggctcatggcggacatggacgcgctcggcaagatctccgccgagtccggggtgccgctgctgcaggacgccgcgcacgcccacggcgcgcgctggcaggggcggcgcgtcggcgagctcggcggcgtcgcggcgttcagcttccagaacggcaaactgatgaccgcgggcgagggcggcgcggtgctgttcccggacggcgacatgtacgagacggcgttcctgcgccacagttgcggccgcccccgggacgaccgccgctacttccaccggatcgcgggctccaacctgcggatgaacgagttctcggcggcggtgctgcgggcccagctgcgccgcctcgacgagcagatcgcggtccgggacgagcgctggaagctgctgtccccgctgctcgcgcggatcgacggcgtcgtcccgcaggcggacgacgtgcgcgccgaccggcactcgcactacatggcgatgttccgcgtccccgggatcaccgaggagcggcgcaacgccctcgtcgaccgcctcgtcgccgccggcgtgcccgcgttcgccgcgttccgcgcgatccaccgcaccgaggcgttctgggagatcggggcgcccgcggagacgccggacgcgatcggggagcgctgccccaacaccgaggccatcagccgcgactgcgtctggctgcaccaccgcgtcctgctggcgggggagccggagatccgcgccgccgccgagatcatcgccgacctcgtggcctccgcatga"},{"id":"Chaxamycin_AHBA","name":"Chaxamycin_AHBA","seq":"--atg--------------------------------------------------------------aacgcgcgacaggca-------ccagaattcccccgctggcctcagtacgacgagaccgagcgggacggcctgatccgtgcgctggaacagggccagtggtggcgcatgggcggcgacgaggtcgactccttcgagcgggagttcgccgagcaccacggcgccccccacgcgctggcggtcaccaacggcacccacgcgctggaactcgccctgcaatgcctgggcgtcggcccgggcaccgaagtcatcgtgccggccttcaccttcatctcctcctcgcaggccgcccagcggctcggcgccgtcgccgtccccgtcgacgtccacccggacacctactgcatcgacgccgccgcggtcgccgccgccgtcaccccccgcacccgcgccatcatgccggtgcacatggccgggctcatcgcggacatggacgccctggcgaagatctccgccgacaccggggtccccctgctccaggacgccgctcacgcccacggtgcccgctggcagggcaaacgcgtcggtgaactgggcagcgtcgccgcgttcagcttccagaacggcaagctgatgaccgccggcgagggcggcgccctcctcttcccggacacggagctgtacgaggcggcgttcctgcggcacagctgcggccgcccccgcgacgaccgccgctacctgcaccgcaccgcgggctccaacctgcgcatgaacgagttctccgcggccgtgctgcgcgcccagctgagccgcctcgaccagcagatcgccctgcgggaggagcgctggaccctgctgtcccggctgctcgccgccatcgacggcgtcgtcccgcagggcggcgacgtgcgcgtggaccgcaagtcgcactacatggcgatgttccgggtccccggcctcggcgaggagcggcgcaacgccctggtcgaccggctcgtggaggcgggcctgcccgcgttcgccgcgttccgcgcgatctaccgcaccgacgccttctgggagatcggcgcacccgacgagacccccgacgccatcgccgcccgctgcccccacaccgaagccatcagccaggactgcgtctggctgcaccaccgcgtcctgctcgccggggaagcggagatgcacgccaccgccgagatcatcgccgacctcgtggcccgcgcgtga"}],"config":{"vis":{"conserv":false,"overviewbox":true,"seqlogo":true,"sequences":true,"markers":true,"metacell":false,"gapHeader":false,"leftHeader":true,"labels":true,"labelName":true,"labelId":false,"labelPartition":false,"labelCheckbox":false,"metaGaps":true,"metaIdentity":true,"metaLinks":true},"conf":{"dropImport":true,"registerMouseHover":false,"registerMouseClicks":true,"eventBus":true,"alphabetSize":20,"debug":false,"hasRef":false,"manualRendering":false},"colorscheme":{"scheme":"nucleotide","colorBackground":true,"showLowerCase":true,"opacity":0.6},"zoomer":{"menuFontsize":"12px","autoResize":true,"alignmentWidth":"auto","alignmentHeight":225,"columnWidth":15,"rowHeight":15,"textVisible":true,"labelIdLength":30,"labelName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4px 3px 4px"}},"menu":true,"features":null},"evals":[],"jsHooks":[]}</script>

---

### Protein structure

[Example](http://www.rcsb.org/3d-view/4hhb/1) using [NGL](https://github.com/arose/ngl): a web application for molecular visualization: display molecules like proteins and DNA/RNA with a variety of representations.

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### Genome browsers

Example from my postdoc (González lab): [DrosOmics](https://www.biologiaevolutiva.org/gonzalez_lab/drosomics/)

Coronado-Zamora M, Salces-Ortiz J, González J. 2023. DrosOmics: A Browser to Explore -omics Variation Across High-Quality Reference Genomes From Natural Populations of *Drosophila melanogaster*. Mol Biol Evol 40:msad075.

---

### Shiny applications

Example: [**ShinyGO**](http://bioinformatics.sdstate.edu/go/): allows in-depth analysis of gene lists, with graphical visualization of enrichment, pathway, gene characteristics and protein interactions.

Ge SX, Jung D & Yao R. 2020. Bioinformatics 36:2628–2629.

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# Wrap-up

- Most basic exploratory and communication graphs in Bioinformatics can be achieved with general-purpose statistical graphics tools
- The complexity and characteristics of some biological data requires specialized tools
    + If static requirements, `ggplot2` extensions may help
    + If interactive requirements, `htmlwidgets` may help
    + Check tools used in similar studies

---